Requirements identification in textual documents or extraction is a tedious and error prone task that many researchers suggest automating. We manually annotated the PURE dataset thus created new one containing both requirements non-requirements. Using this dataset, we fine-tuned BERT model compare results with several baselines such as fastText ELMo. In order to evaluate on semantically more co...